The University of Virginia Health System is testing a new artificial intelligence platform that is integrated with its picture archiving and communication system with the aim of streamlining workflow and detecting findings that radiologists may not notice.

The AI software, from vendor Zebra Medical Vision, also colors an icon with its findings – green if results are normal and red if results are abnormal – which expedites reading workflows in the PACS, from vendor Carestream Health. This is ideal for radiologists because with one glance at the screen they know whether they need to review results, said Cree Gaskin, MD, professor of radiology, medical imaging and orthopedic surgery, vice-chair of operations and informatics, and associate chief medical information officer at the University of Virginia Health System.

“A key benefit is that the AI software searches certain imaging studies for several specific conditions,” Gaskin explained. “It calculates liver density from a CT chest or abdomen study to detect a fatty liver, identifies emphysema by detecting levels of trapped air in the lungs from a CT chest study, and determines levels of coronary calcium from non-contrast CT chest studies, in addition to assessing bone density.”

So while a radiologist is examining the image for a suspected condition, the software can alert the radiologist to examine a secondary condition, Gaskin said. “This can lead to early detection of diseases and conditions that might go unnoticed, which represents an exciting step forward in preventative care,” Gaskin added.

Clearly, artificial intelligence is playing a role that goes beyond what humans today are able to do. And it’s a key role if it can prevent serious illnesses from developing.

“When radiologists are reading an exam, they try to be objective but are also appropriately focused on any diseases or conditions that might cause symptoms that the patient is experiencing,” Gaskin said. “But we do miss findings, and this may be more common if they are incidental and not the focus of the exam.”

Sophisticated AI algorithms potentially can deliver the added advantage of detecting findings that may go unnoticed or are difficult to visualize.

“Bone density is a perfect example,” Gaskin said. “The algorithm performs a computational assessment and determines whether the result is abnormal. The radiologist then views the bone structure and confirms the finding. Without the algorithm, the radiologist might not have detected the abnormal bone density. This can lead to preventative care to ensure that the condition is treated.”

Gaskin is quite excited about where AI may lead radiologists in the future.

“From the radiologist’s perspective, the next step in this process is the development of more powerful algorithms to aid in lesion detection,” Gaskin said. “The software will need to highlight the specific finding rather than simply saying that a medical finding is present.”

Another important factor is smooth AI integration with PACS, Gaskin added.

“The software cannot slow down the reading process – it needs to be running in the background and presenting clinically relevant findings that might have gone undetected,” Gaskin said.